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Flatiron Books AI Review

Flatiron Books AI Review

Table of Contents

Have you ever wondered what exactly artificial intelligence (AI) is, where we currently stand in its development, and where we might be headed? Well, we certainly have! That’s why we were intrigued by “Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021”. Let’s dive into this insightful read together, shall we? This book promises to untangle the complexities of AI in a way that is engaging and accessible.

Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021

Discover more about the Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021.

An Overview of the Book

We all know that AI is a buzzword these days. From our smartphones to our homes, it seems to be everywhere. But breaking the concept down into digestible parts can be daunting. Thankfully, this book does a superb job at that.

A Journey Through Time

We begin with the title itself. “A Brief History of Artificial Intelligence” sets the stage for a historical tour. As we journey through time, the book covers key milestones in the development of AI, giving us a clearer understanding of how we got from zero to self-driving cars and digital assistants.

Where We Are Now

The book isn’t just a trip down memory lane. It meticulously details the current state of AI, from autonomous machinery to personal assistants like Siri and Alexa. We learn about the algorithms that power these wonders and the companies that are on the forefront of these innovations.

What Lies Ahead

What really fascinated us was the book’s ability to project into the future. What does the future hold for AI? From AI ethics to superintelligent systems, the possibilities seem both thrilling and, to be frank, a little daunting. But don’t worry, this book provides a level-headed analysis that will leave us feeling more informed than anxious.

Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021

Get your own Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021 today.

Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021

Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021

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Detailed Breakdown

To make our review easier to digest, let’s break the book down into more palatable sections.

Historical Context of AI

This book dives into the origins of AI, taking us through its inception in ancient myths and legends, all the way to Alan Turing’s groundbreaking ideas in the 20th century. It even mentions the term “Artificial Intelligence” being coined by John McCarthy in 1956.

Year Key Event Impact
1950 Turing Test Introduced a standard for AI intelligence
1956 The term “Artificial Intelligence” coined Formal introduction of AI as a field
1980s Expert Systems AI’s practical applications in businesses
2000s Machine Learning Revolution Shift towards data-driven AI
2010s Deep Learning and Big Data Major advancements in AI’s capabilities

This chronological layout helps us grasp just how far AI has come in a relatively short period of time.

Core Technologies

One of the strengths of the book is its explanation of core AI technologies—machine learning, neural networks, and natural language processing (NLP), among others.

Machine Learning

Machine learning is unpacked in a way that feels like chatting with a knowledgeable friend rather than wading through academic jargon. We learn about supervised, unsupervised, and reinforcement learning—each described in a manner that’s easy to understand.

Neural Networks

Neural networks, those enigmatic constructs mimicking our brain’s architecture, are demystified here. The book takes the time to explain how these multi-layered networks learn from data and iteratively get better at tasks, making it clear why they are the backbone of modern AI.

Natural Language Processing (NLP)

NLP is yet another highlight. Ever wondered how your phone’s voice assistant understands your speech? It’s all due to NLP, and the book does an excellent job of breaking this down into layman’s terms, explaining how machines are trained to understand and generate human language.

Real-World Applications

The book impressively transitions from theory to practice by detailing AI’s real-world applications. From healthcare to finance, and from autonomous vehicles to personalized marketing—this section makes us see the tangible benefits (and occasional nightmares) of AI.

Healthcare

Who hasn’t shuddered at the idea of robot doctors? Yet, the book reveals the substantial benefits already being reaped in diagnostics, treatment plans, and even robotic surgery. It’s transformative, we’re told, and though we’re still early in this journey, the potential benefits could revolutionize healthcare.

Finance

In the volatile world of stocks and trading, AI is making waves. Automated trading systems, fraud detection algorithms, and personalized banking experiences are taking the industry by storm. We get a nuanced view of how AI is both an aid and a disruptor in finance.

Sector Application Example
Healthcare Diagnostics, Robotic Surgery AI algorithms diagnosing diseases with high accuracy
Finance Automated Trading, Fraud Detection Algorithm-based stock trading, real-time fraud detection
Transportation Autonomous Vehicles Self-driving cars navigating in real-world scenarios
Retail Personalized Marketing AI-driven product recommendations on e-commerce platforms

Ethical Considerations

Of course, no book on AI would be complete without tackling the deep ethical questions—bias, privacy concerns, and the socio-economic impact. Are robots going to take our jobs? Are we entering a surveillance state? This section dives into these critical issues, laying out both the promises and perils of AI.

Bias and Fairness

We’re keenly aware of how algorithms can perpetuate existing biases. By reading this book, we get to understand how crucial it is to design unbiased systems and the challenges involved in doing so.

Privacy Concerns

With advancements in AI come new ways of collecting and using data. The book doesn’t shy away from the delicate balance between convenience and privacy, a topic particularly relevant in today’s world of social media and data breaches.

Economic and Social Impact

The potential for AI to create economic disruption is enormous. From the future of work to the creation of new job categories, the analysis is both detailed and balanced, making it clear that while AI offers numerous opportunities, it also presents significant challenges.

Future of AI

The last part of the book is perhaps the most speculative yet engrossing. It presents different scenarios of what the future might hold for AI. Will we reach Artificial General Intelligence (AGI)? What would a world governed by superintelligent AI look like? These thought-provoking sections make us reflect on our own roles and responsibilities in shaping that future.

Artificial General Intelligence (AGI)

The idea of AGI—intelligence that rivals human cognitive capabilities—fascinates and terrifies in equal measure. By reading this book, we gain a sense of how imminent AGI might be, and what steps are being taken to either hasten or delay its arrival.

Superintelligent Systems

As we wade deeper into the subject, we encounter the concept of superintelligence—AI that surpasses human intellect in every domain. This part is full of hypotheticals, urging us to ponder the philosophical and ethical questions of creating something potentially beyond our control.

Final Thoughts and Recommendations

By the end of our read, it’s clear that “A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going” is more than just a quick primer. It’s an in-depth yet approachable guide that makes a complex subject accessible to all of us.

If you’re curious about AI, or perhaps a little anxious about what the future holds, this book is an excellent place to start. It’s well-researched, thoughtfully organized, and written in a way that feels like a conversation with a knowledgeable friend. Whether you’re tech-savvy or a complete novice, the insights offered here will surely expand your understanding and appreciation for the marvel that is AI.

We found the journey through the past, present, and future of AI to be deeply enriching and highly recommend it to anyone interested in understanding one of the most transformative technologies of our time. So, let’s not wait for the robots to take over—let’s get a jumpstart on understanding AI today!

Click to view the Flatiron Books A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going Hardcover – Big Book, 19 January 2021.

Disclosure: As an Amazon Associate, I earn from qualifying purchases.

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Emmanuel Bazile All Rights Reserved

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (B = 0.87, p < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (B = 0.02, p = .423). Additionally, CSE (B = 0.02, p = .423), PC (B = 0.05, p = .334), ATE (B = 0.00, p = .983), and PP (B = 0.03, p = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (r = 0.22, p < .001) and RES (r = 0.21, p < .001). The results also indicated that PC was significantly correlated with ATE (r = -0.79, p < .001), AXY (r = 0.18, p < .001), and RES (r = 0.20, p < .001). ATE was significantly correlated with AXY (r = -0.19, p < .001) and RES (r = -0.19, p < .001). AXY was significantly correlated with RES (r = 0.85, p < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, p < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, p < .001), ATE (F(7,194) = 2.20, p = .036), Years computers (F(1,194) = 5.71, p = .018), and PC (F(12,194) = 2.00, p = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.